Fuzzy Output Feedback Research Articles

Data-Driven Learning Fuzzy Output-Feedback Control with Prescribed Performance for Nonlinear Systems

Data-Driven Learning Fuzzy Output-Feedback Control with Prescribed Performance for Nonlinear Systems

Distributed output feedback fuzzy secure consensus control for nonlinear MASs against DoS attacks

The multi-agent systems (MASs) exchange the communication information by the wireless sensor network and communication network, which are vulnerable to denial-of-service (DoS) attacks. Since the DoS attacks can interrupt the sensor channels and communication channels, and result in the instability of MASs, to study the distributed adaptive fuzzy secure consensus control (SCC) problem for nonlinear MASs under DoS attacks is momentous. This article investigates the distributed adaptive fuzzy SCC issue for nonlinear MASs with DoS attacks for sensor channels and communication channels. Because the sensor channels and communication channels are interrupted by DoS attacks, the system states, the leader and its high-order derivatives are unavailable, a switching-type fuzzy state observer and distributed observer are first established to estimate them. Based on the proposed switching-type fuzzy state observer and distributed observer, a distributed adaptive SCC approach is proposed by backstepping control design theory and fuzzy logic systems (FLSs). It is proved that the errors of the designed observers converge to zero's a small neighborhood and the controlled nonlinear MASs are stable. Moreover, each follower is able to track the leader. Finally, we apply the proposed control approach to multiple unmanned surface vehicles (USVs), the simulation results verify its effectiveness.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

Adaptive neural chattering suppression pseudo inverse active control for a class of micromilling systems with multiaxis piezoelectric actuators

For a multi-axis nonlinear milling system with piezoelectric actuators, a pseudo inverse dynamic surface active control scheme with adaptive fuzzy output-feedback is developed. The main contributions can be characterized as following: (1) to overcome the hysteresis non-linearities in the piezoelectric actuators, the new hysteresis pseudo inverse compensators are established to replace the conventional hysteresis direct-inverse model construction, which is a very challenging or even impossible work. The hysteresis pseudo inverse means that the hysteresis direct-inverse model is no longer essential any more, instead, a search algorithm for the actual control signal from the designed hysteretic temporary control laws are proposed to alleviate the hysteresis nonlinearities effectively; (2) to realized high-precision tracking control and mitigate chattering suppression in the micromilling systems, the fuzzy logic systems, the novel dynamic surface control algorithms and the modified high-gain states observer are designed to approximise the unknown continuous functions of the micromilling systems, design the high performance controller and estimate the immeasurable states in the control system, respectively; (3) the hardware-in-loop simulation platform is constructed and the experiments of tracking control and chattering suppression are implemented to validate the effectivity of the developed control scheme.

Finite-time fuzzy adaptive consensus control for state-constrained MIMO nonlinear MASs with switched topologies

In this article, the adaptive fuzzy finite-time output feedback consensus control problem is investigated for multiple input and multiple output (MIMO) nonlinear multi-agent systems (MASs) with switched topologies and asymmetric state constraints. Fuzzy logic systems (FLSs) are utilised to approximate uncertain nonlinear dynamics, and a distributed observer and a state observer are established to estimate the unknown leader and system states, respectively. By constructing the barrier Lyapunov functions (BLFs), a finite-time output feedback consensus control scheme is presented via backstepping control technique. It is proved that the controlled MASs are stable, and the consensus errors converge in finite time. Moreover, the system states will not exceed the given bounds. Finally, the proposed finite-time consensus control approach is applied to control multiple unmanned surface vehicles (USVs), the simulation results check the feasibility of the developed finite-time consensus control methodology.

Predefined-time Fuzzy Output Feedback Control for Nonlinear Systems with Multiple Actuator Constraints

Predefined-time Fuzzy Output Feedback Control for Nonlinear Systems with Multiple Actuator Constraints

Prescribed performance adaptive fuzzy output feedback control for steer-by-wire vehicle system with intermittent actuator faults

Prescribed performance adaptive fuzzy output feedback control for steer-by-wire vehicle system with intermittent actuator faults

Fuzzy control for networked systems with mixed data missing under quantization based event-triggered scheme

This paper presents an innovative event-triggered fuzzy control approach for networked systems operating over unreliable network channels. Traditional complicated modeling and analysis process of network induced issues is replaced by a bran-new logarithmic quantization based event-triggered scheme method. The joint characterization of mixed data missing, arising from the event-triggered mechanism and unreliable network channels, becomes a challenging issue due to disrupted data sequences and variable relations. Consequently, this paper contributes a formulated auxiliary stochastic sequence approach to overcome the difficulty of dynamics modeling. With an formulated event driven controller, the modeling and implementation of the closed-loop systems are constructed and further simplified. Considering the difference and relation between the membership functions of plant and controller, we present a new fuzzy output feedback controller design method to obtain feasible result via uncertainty based method. Finally, we verify the validity of the designed control policy via a simulation example.

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Finite-Time Adaptive Fuzzy Backstepping Control for Quadrotor UAV With Stochastic Disturbance

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat{\rm \textbf{o}}$</tex-math> </inline-formula> differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

Adaptive fuzzy finite-time output feedback fault-tolerant control for MIMO stochastic nonlinear systems with non-affine nonlinear faults

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy.

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization

Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

Robust fuzzy adaptive stabilization for uncertain nonlinear systems with quantized input and output constraints

In this paper, we study the problem of robust fuzzy adaptive output feedback stabilization for a class of nonlinear systems with quantized input and output constraints. The system under consideration has these characteristics: unknown state mismatch uncertainties and unknown disturbances, and unknown measurement noises. By employing the output filtering transformation, the fuzzy logic system (FLS) approximator, the backstepping, and the high gain observer technique, a non-fragile robust fuzzy adaptive observer and a hyperbolic tangent adaptive controller are presented to guarantee that the closed-loop system is ultimately uniformly bounded and the output is constricted in a prescribed performance curve. The effectiveness of the scheme is verified by numerical simulations.

Optimal fuzzy output feedback tracking control for unmanned surface vehicles systems

This study investigates an optimal fuzzy output feedback tracking control problem with a Q-learning algorithm on unmanned surface vehicles (USVs) with immeasurable velocities information. Firstly, the USVs are modeled by a Takagi–Sugeno (T–S) fuzzy system. Based on the fuzzy system, an ideal optimal fuzzy output feedback tracking controller is proposed by utilizing reconstruction states, measured output and input data. Theoretically, the analytical solution of the designed optimal controller is reduced to solving the algebraic Riccati equations (AREs). Due to the difficulty of solving AREs directly, a Q-learning value iteration algorithm is proposed to obtain its approximation solution. Furthermore, it is rigorously mathematically proved that the proposed Q-learning algorithm can approximate the ideal solution and guarantee the USVs output to track the desired reference signal. Ultimately, the simulation and comparison results with the existing control method verify the efficacy and advantage of the presented fuzzy optimal output feedback tracking control approach with a Q-learning algorithm.

Adaptive type-2 fuzzy output feedback control using nonlinear observers for permanent magnet synchronous motor servo systems

This paper investigates the accurate servo position control of the permanent magnet synchronous motor (PMSM)-driven system under varying external disturbance and unknown mechanical friction. Firstly, to overcome the difficulty of state observer design caused by uncertainties, the interval type-2 fuzzy logic system (IT2 FLS) is introduced to approximate the nonlinear friction. Meanwhile, the nonlinear disturbance observer (NDO) is employed to estimate the lumped disturbance. Then aiming to solve the problem of explosion of complexity and filtering error, a finite-time (FT) adaptive IT2 fuzzy output feedback controller is proposed by combining the error compensation mechanism and FT control. In addition, to further improve the performance of the closed-loop system, the adaptive law is updated by the compensation error signal. By means of Lyapunov stability theory, all signals in the closed loop system can be guaranteed to be finite-time stable. Numerical simulations and real-time experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

Adaptive Fuzzy Output Feedback Fault-Tolerant Control for Active Suspension Systems

In this paper, a novel adaptive fuzzy output feedback fault-tolerant control (FTC) method is proposed for active suspension systems. The quarter active suspension systems studied in this paper contains electromagnetic actuator faults and unavailable state variables, such as the velocity of active suspensions and the electric current intensity of electromagnetic actuators. First, the complex spring nonlinear dynamics are approximated by fuzzy logic systems (FLSs), and the unmeasurable states are estimated by a fuzzy state observer. Based on an adaptive backstepping technique along with specific Lyapunov functions, a novel adaptive fuzzy output feedback FTC design is presented. To eliminate the need for knowing efficiency indexes, a fault compensation strategy is also developed by utilizing parameter estimation technique. In the presence of electromagnetic actuator failure, all vertical vibration states of the active suspension system remain stabilized. Eventually, the feasibility of the proposed control algorithm is evaluated through random road surface and bump road surface tests.

Adaptive fuzzy finite-time event-triggered formation control for unmanned surface vehicle systems

This paper studies the finite-time event-triggered fuzzy formation control problem for multiple unmanned surface vehicle (USV) system. The fuzzy state estimator is designed by intermittent output signal to estimate unmeasurable velocity and yaw velocity of the USV. The dual-channel event-triggered mechanisms (ETMs) are designed to reduce the updating times from sensor-to-actuator and controller-to-actuator channels, respectively. Based on the designed fuzzy state estimator and ETMs, a finite-time fuzzy adaptive output feedback formation control is formulated. It is proved that the controlled USV system is semi-global practically finite-time stable. Moreover, the outputs of the followers are able to track the output of the leader. Finally, the simulation and comparison results are provided to demonstrate the feasibility of the presented formation control algorithm.

The stochastic stability and H∞‐fuzzy control of stochastic bifurcation of a doubly‐fed induction generator

AbstractConsidering the dynamic behavior of doubly‐fed induction generators (DFIGs) under the influence of random factors, this paper not only analyzes the phenomenon of stochastic instability and bifurcation of a DFIG dynamic variable in its random space when they are affected by environmental noise, but also proposes a method based on the Tkagi–Sugneo (T–S) fuzzy control strategy to control its stochastic bifurcation. First, a four‐dimensional stochastic dynamic DFIG model is established by using multiplicative white noise to simulate the influence of environmental noise on electrical variables, and stochastic central manifold theory is used to reduce the dimensionality of a planar model in the bifurcation neighborhood. Then, the stochastic stability of the model is investigated based on singular boundary theory, while the steady‐state probability density of the stochastic DFIG is determined using the Fokker–Plank–Kolmogorov equation to obtain the location and probability density of its stochastic P‐bifurcation. Finally, the influence of stochastic bifurcation behavior is eliminated by H∞‐fuzzy output feedback control. The numerical simulation results indicate that the location and probability of stochastic bifurcation in a DFIG will vary with the change in noise intensity, and the bifurcation parameter values and stochastic stability domain are obtained. The harm caused by random factors can be solved based on H∞‐fuzzy output feedback, which provides a theoretical basis for the stable operation of the DFIG system.

Predefined-time fuzzy adaptive output feedback control for non-strict feedback stochastic nonlinear systems with state constraints

Predefined-time fuzzy adaptive output feedback control for non-strict feedback stochastic nonlinear systems with state constraints